Neurology

Cognitive impairment screening strategy to reduce the burden of Alzheimer's disease in Shanghai: A system dynamics approach.

BACKGROUND: Population aging increases the economic burden of Alzheimer's disease (AD). Early screening for mild cognitive impairment (MCI) has the potential to mitigate this burden, but optimal strategies regarding screening coverage and age targeting remain unclear.

Effects of exoskeleton rehabilitation robot training on neuroplasticity and lower limb motor function in patients with stroke.

BACKGROUND: Lower limb exoskeleton rehabilitation robot is a new technology to improve the lower limb motor function of stroke patients. Recovery of motor function after stroke is closely related to neuroplasticity in the motor cortex and associated motor areas. However, few studies investigate how rehabilitation robots affect the neuroplasticity of stroke patients.This study sought to determine t...

Development of a Novel Machine Learning Model to Automate Vertebral Column Segmentation Utilizing Biplanar Full-body Imaging.

BACKGROUND CONTEXT: Degenerative scoliosis (DS) is a common spinal disorder among adults, characterized by lateral curvature of the spine. Recent adva...

Optimizing stroke lesion segmentation: A dual-approach using Gaussian mixture models and nnU-Net.

Machine learning-based stroke lesion segmentation models are widely used in biomedical imaging, but their ability to detect treatment effects remains ...

Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.

Neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), present significant challenges owing to their complex pathologies and a lac...

Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to ach...

Automated Quantification of Cerebral Microbleeds in SWI: Association with Vascular Risk Factors, White Matter Hyperintensity Burden, and Cognitive Function.

BACKGROUND AND PURPOSE: The amount and distribution of cerebral microbleeds (CMB) are important risk factors for cognitive impairment. Our objective w...

Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.

BACKGROUND AND PURPOSE: The diagnostic performance of deep learning model that simultaneously detecting and quantifying nigrosome-1 abnormality by usi...

Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF.

BACKGROUND AND PURPOSE: The automatic recognition of intracraial aneurysms by means of machine-learning algorithms represents a new frontier for diagn...

A depression detection approach leveraging transfer learning with single-channel EEG.

Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine ...

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data i...

Assessing the Content of Goals of Care Documentation for Hospitalized Patients With Alzheimer's Disease and Related Dementias.

BACKGROUND: Goals of care (GOC) conversations are an evidence-based practice that help clarify and align patient values and preferences for medical ca...

Conditional Generative Models for Simulation of EMG During Naturalistic Movements.

Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and rem...

GBE1 alleviates MPTP-induced PD symptoms in mice by enhancing glycolysis and oxidative phosphorylation.

In Parkinson's disease (PD), the disturbance of energy metabolism due to glucose metabolic reprogramming may be a critical factor contributing to neur...

Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data.

Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This...

Industrial-grade collaborative robots for motor rehabilitation after stroke and spinal cord injury: a systematic narrative review.

BACKGROUND: There is a growing interest in exploring industrial-grade collaborative robots (cobots) for rehabilitation. This review explores their app...

Discovering Novel Biomarkers and Potential Therapeutic Targets of Amyotrophic Lateral Sclerosis Through Integrated Machine Learning and Gene Expression Profiling.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has multiple factors that make its molecular pathogenesis difficu...

Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study.

BACKGROUND: Cognitive impairment, indicative of Alzheimer disease and other forms of dementia, significantly deteriorates the quality of life of older...

Factors influencing short-term and long-term survival rates in stroke patients receiving enteral nutrition: a machine learning approach using MIMIC-IV database.

PURPOSE: This study aims to evaluate the survival and mortality rates of stroke patients after receiving enteral nutrition, and to explore factors inf...

Proteomic profiling in cerebrospinal fluid reveal biomarkers for shunt outcome in idiopathic normal-pressure hydrocephalus.

BACKGROUND: The pathophysiology of idiopathic normal pressure hydrocephalus (iNPH) remains unclear, and the treatment strategy remains suboptimal. Thi...